We propose a novel image dataset focused on tiny faces wearing face masks for mask classification purposes, dubbed Small Face MASK (SF-MASK), composed of a collection made from 20k low-resolution images exported from diverse and heterogeneous datasets, ranging from 7 x 7 to 64 x 64 pixel resolution. An accurate visualization of this collection, through counting grids, made it possible to highlight gaps in the variety of poses assumed by the heads of the pedestrians. In particular, faces filmed by very high cameras, in which the facial features appear strongly skewed, are absent. To address this structural deficiency, we produced a set of synthetic images which resulted in a satisfactory covering of the intra-class variance. Furthermore, a small subsample of 1701 images contains badly worn face masks, opening to multi-class classification challenges. Experiments on SF-MASK focus on face mask classification using several classifiers. Results show that the richness of SF-MASK (real + synthetic images) leads all of the tested classifiers to perform better than exploiting comparative face mask datasets, on a fixed 1077 images testing set. Dataset and evaluation code are publicly available here: https://github.com/HumaticsLAB/sf-mask

翻译：我们提出了一种专注于微小戴口罩人脸分类的新型图像数据集，命名为小尺寸口罩数据集（SF-MASK）。该数据集包含从多种异构数据集中提取的2万张低分辨率图像，分辨率范围从7×7到64×64像素。通过计数网格的可视化分析发现，该数据集中行人头部姿态多样性存在显著缺失。具体而言，由超高摄像头拍摄的面部特征严重倾斜的人脸图像未被涵盖。为解决这一结构性缺陷，我们生成了一组合成图像，有效覆盖了类内方差。此外，包含1701张图像的小规模子样本中包含了佩戴不规范口罩的情况，为多类别分类任务提供了新挑战。在SF-MASK上开展的实验聚焦于使用多种分类器进行口罩分类。结果表明，在固定1077张测试集上，SF-MASK（真实图像+合成图像）的丰富性使得所有测试分类器的性能均优于利用其他可比口罩数据集的情况。数据集与评估代码已在以下地址开源：https://github.com/HumaticsLAB/sf-mask